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Flirting Is Body language Too

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When we flirt, we may think most about what we are going to say. We want the words to come outright to impress the person we are interested in, but it is important that we not forget to pay attention to our body language.

What you say with your body is just as important as what you say with your mouth. The right body language can convey so much to another person. As they say- a picture is worth a thousand words- so too does our body convey a lot without us having to say anything. Just smiling can have a lot of meaning in a conversation. Here are a few tips about how your body language affects flirting.

Your Smile Says Plenty

We mentioned the smile already, so let’s start with that. When you smile, you signal that you are listening to what the other person is saying and showing interest. Sometimes, it can be hard for people to pick up visual and verbal cues. They may not know if you like them or if you’re interested in what they’re saying. A smile is an obvious way to show interest and friendliness as well. It makes you look more welcoming and can get rid of any standoff-ish vibes you may have been putting out.

Your Feet Point Where You Want to Go

Did you know that your feet can tell someone where you’re interested in going even when you’re sitting or standing? People will reflexively and unconsciously point their feet where they want to go to.

If you’re looking to leave, you may point your feet toward the door, even if you’re having a conversation with someone. People might not notice where your feet are pointing if they’re looking at your face, but their mind we can pick up subconsciously what you are doing and where you want to go. You may be giving off verbal cues that you’re not even realizing.

If your feet are tensed up or pointed, that can show discomfort and make you seem impenetrable. If you look more relaxed, that can indicate that you’re paying attention and you are at ease.

If you want to flirt more successfully, pay attention to your feet and the body language you’re giving off. Try to keep your feet pointed toward the person you’re talking to and not toward the door.

Your Hands Tell a Story

The way you move your hands can show whether you’re comfortable or uncomfortable with someone. As you are using some of the flirting techniques discussed in มุขเสี่ยว, be careful about the messages you’re sending with your hands. If you’re holding your hands tightly, clenching them, wringing them or fidgeting nervously, all of that can indicate that you may not be interested in the person you’re talking to. Of course, it could indicate that you’re just nervous because you like them so much, but to the other person, it can make you seem uncomfortable and like you might not be attracted to them.

Moving your hands while you speak to accentuate what you’re saying and show interest in the conversation and can be a sign of a good conversationalist. If you don’t move your hands at all when you talk, that can make you seem tired, disinterested, or simply a poor conversationalist.

You may want to videotape yourself speaking to see what kind of hand movements you make. You could also try practicing talking in the mirror and look at your body language and particularly at your hands. They do tell a story, and other people can pick up on how you feel based on what your hands are doing.

Know Your Angles

Not all body language has to do with movement, when we’re talking about flirting. Sometimes, just how you hold and angle yourself can have an impact on how the conversation goes. You should know what your most attractive or desirable angle is. We want to make sure that your body catches the light in the right way and that you’re showing your best attributes off as you flirt. All of this can make you more attractive and appealing to the opposite sex.

 

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Today, Meta announced that it has built and open sourced ‘No Language Left Behind’ NLLB-200, a single AI model that is the first to translate across 200 different languages, including 55 African languages with state-of-the-art results. Meta is using the modelling techniques and learnings from the project to improve and extend translations on Facebook, Instagram, and Wikipedia.

 

In an effort to develop high-quality machine translation capabilities for most of the world’s low-resource languages, this single AI model was designed with a focus on African languages. They are challenging from a machine translation perspective. AI models require lots and lots of data to help them learn, and there’s not a lot of human translated training data for these languages. For example, there’s more than 20M people who speak and write in Luganda but examples of this written language are extremely difficult to find on the internet.

 

We worked with professional translators for each of these languages to develop a reliable benchmark which can automatically assess translation quality for many low-resource languages. We also work with professional translators to do human evaluation too, meaning people who speak the languages natively evaluate what the AI produced. The reality is that a handful of languages dominate the web, so only a fraction of the world can access content and contribute to the web in their own language. We want to change this by creating more inclusive machine translations systems – ones that unlock access to the web for the more than 4B people around the world that are currently excluded because they do not speak one of the few languages content is available in.

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“It’s impressive how much AI is improving all of our services. We just open-sourced an AI model we built that can translate across 200 different languages — many of which aren’t supported by current translation systems. We call this project No Language Left Behind, and the AI modelling techniques we used are helping make high quality translations for languages spoken by billions of people around the world. To give a sense of the scale, the 200-language model has over 50 billion parameters, and we trained it using our new Research SuperCluster, which is one of the world’s fastest AI supercomputers. The advances here will enable more than 25 billion translations every day across our apps. Communicating across languages is one superpower that AI provides, but as we keep advancing our AI work it’s improving everything we do — from showing the most interesting content on Facebook and Instagram, to recommending more relevant ads, to keeping our services safe for everyone,” said Meta CEO Mark Zuckerberg in a post on his Facebook profile.

 

Language is our culture, identity, and lifeline to the world. However, as high-quality translation tools don’t exist for hundreds of languages, billions of people today can’t access digital content or participate fully in conversations and communities online in their preferred or native languages. This is especially true for hundreds of millions of people who speak the many languages of Africa.

 

“Africa is a continent with very high linguistic diversity, and language barriers exist day to day. We are pleased to announce that 55 African languages will be included in this machine translation research, making it a major breakthrough for our continent,” Balkissa Ide Siddo, Public Policy Director for Africa said while speaking about the launch of the AI model. “In the future, imagine visiting your favourite Facebook group, coming across a post in Igbo or Luganda, and being able to understand it in your own language with just a click of a button – that’s where we hope research like this leads us. Highly accurate translations in more languages could also help to spot harmful content and misinformation, protect election integrity, and curb instances of online sexual exploitation and human trafficking.”

 

While commenting on accessibility and inclusion in the pursuit of building an equitable metaverse, Ide Siddo added “At Meta, we are working today to ensure that as many people as possible will be able to access the new educational, social and economic opportunities that the next evolution of the internet will bring to future technology and an everyday living experience tomorrow.”

 

To confirm that the translations are high quality, Meta also created a new evaluation dataset, FLORES-200, and measured NLLB-200’s performance in each language. Results revealed that NLLB-200 exceeds the previous state of the art by an average of 44 percent.

 

Meta is also open-sourcing the NLLB-200 model and publishing a slew of research tools to enable other researchers to extend this work to more languages and build more inclusive technologies. Meta AI is also providing up to $200,000 of grants to non-profit organizations for real world applications for NLLB-200.

 

There are versions of Wikipedia in more than 300 languages, but most have far fewer articles than the 6+ million available in English. Following Meta’s partnership with the Wikimedia Foundation, the non-profit organization that hosts Wikipedia and other free knowledge projects, modelling  techniques and learnings from the NLLB research are now also being applied to translation systems used by Wikipedia editors. Using the Wikimedia Foundation’s Content Translation Tool, articles can now be easily translated in more than 20 low-resource languages (those that don’t have extensive datasets to train AI systems), including 10 that previously were not supported by any machine translation tools on the platform.

 

To explore a demo of NLLB-200 showing how the model can translate stories from around the world, visit here. You can also read the research paper here.

 

 

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